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MLOps Engineer

MLOps Engineer

Cozzera

5 years

0 Lacs

India

Posted:4 months ago| Platform:

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Skills Required

design deployment maintenance learning gcp ml vertex ai dataflow automate training monitoring data experimentation model drift docker kubernetes jenkins bitbucket security versioning governance compliance documentation audit engineering programming python algorithms xgboost orchestration communication

Work Mode

Remote

Job Type

Full Time

Job Description

Position Title:

Experience:

Location:

Employment Type:

About the Role:

We are looking for an experienced MLOps Engineer to lead the design, deployment, and maintenance of scalable and production-grade machine learning infrastructure. The ideal candidate will have a strong foundation in MLOps principles, expertise in GCP (Google Cloud Platform), and a proven track record in operationalizing ML models in cloud environments.

Key Responsibilities:

Design, build, and maintain scalable ML infrastructure on GCP using tools such as Vertex AI, GKE, Dataflow, BigQuery, and Cloud Functions.
Develop and automate ML pipelines for training, validation, deployment, and monitoring using Kubeflow Pipelines, TFX, or Vertex AI Pipelines.
Collaborate closely with Data Scientists to transition models from experimentation to production.
Implement robust monitoring systems for model drift, performance degradation, and data quality issues.
Manage containerized ML workloads using Docker and Kubernetes (GKE).
Set up and manage CI/CD workflows for ML systems using Cloud Build, Jenkins, Bitbucket, or similar tools.
Ensure model security, versioning, governance, and compliance throughout the ML lifecycle.
Create and maintain documentation, reusable templates, and artifacts for reproducibility and audit readiness.

Required Skills & Experience:

Minimum 5 years of experience in MLOps, ML Engineering, or related roles.
Strong programming skills in Python with experience in ML frameworks and libraries.
Hands-on experience with GCP services including Vertex AI, BigQuery, GKE, and Dataflow.
Solid understanding of machine learning concepts and algorithms such as XGBoost and classification models.
Experience with container orchestration using Docker and Kubernetes.
Proficiency in implementing CI/CD practices for ML workflows.
Strong analytical, problem-solving, and communication skills.

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MLOps Engineer